{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ALLOW_THREADS', 'AxisError', 'BUFSIZE', 'Bytes0', 'CLIP', 'ComplexWarning', 'DataSource', 'Datetime64', 'ERR_CALL', 'ERR_DEFAULT', 'ERR_IGNORE', 'ERR_LOG', 'ERR_PRINT', 'ERR_RAISE', 'ERR_WARN', 'FLOATING_POINT_SUPPORT', 'FPE_DIVIDEBYZERO', 'FPE_INVALID', 'FPE_OVERFLOW', 'FPE_UNDERFLOW', 'False_', 'Inf', 'Infinity', 'MAXDIMS', 'MAY_SHARE_BOUNDS', 'MAY_SHARE_EXACT', 'MachAr', 'ModuleDeprecationWarning', 'NAN', 'NINF', 'NZERO', 'NaN', 'PINF', 'PZERO', 'RAISE', 'RankWarning', 'SHIFT_DIVIDEBYZERO', 'SHIFT_INVALID', 'SHIFT_OVERFLOW', 'SHIFT_UNDERFLOW', 'ScalarType', 'Str0', 'Tester', 'TooHardError', 'True_', 'UFUNC_BUFSIZE_DEFAULT', 'UFUNC_PYVALS_NAME', 'Uint32', 'VisibleDeprecationWarning', 'WRAP', 'abs', 'absolute', 'add', 'add_docstring', 'add_newdoc', 'add_newdoc_ufunc', 'alen', 'all', 'allclose', 'alltrue', 'amax', 'amin', 'angle', 'any', 'append', 'apply_along_axis', 'apply_over_axes', 'arange', 'arccos', 'arccosh', 'arcsin', 'arcsinh', 'arctan', 'arctan2', 'arctanh', 'argmax', 'argmin', 'argpartition', 'argsort', 'argwhere', 'around', 'array', 'array2string', 'array_equal', 'array_equiv', 'array_repr', 'array_split', 'array_str', 'asanyarray', 'asarray', 'asarray_chkfinite', 'ascontiguousarray', 'asfarray', 'asfortranarray', 'asmatrix', 'asscalar', 'atleast_1d', 'atleast_2d', 'atleast_3d', 'average', 'bartlett', 'base_repr', 'binary_repr', 'bincount', 'bitwise_and', 'bitwise_not', 'bitwise_or', 'bitwise_xor', 'blackman', 'block', 'bmat', 'bool8', 'bool_', 'broadcast', 'broadcast_arrays', 'broadcast_shapes', 'broadcast_to', 'busday_count', 'busday_offset', 'busdaycalendar', 'byte', 'byte_bounds', 'bytes0', 'bytes_', 'c_', 'can_cast', 'cast', 'cbrt', 'cdouble', 'ceil', 'cfloat', 'char', 'character', 'chararray', 'choose', 'clip', 'clongdouble', 'clongfloat', 'column_stack', 'common_type', 'compare_chararrays', 'compat', 'complex128', 'complex64', 'complex_', 'complexfloating', 'compress', 'concatenate', 'conj', 'conjugate', 'convolve', 'copy', 'copysign', 'copyto', 'core', 'corrcoef', 'correlate', 'cos', 'cosh', 'count_nonzero', 'cov', 'cross', 'csingle', 'ctypeslib', 'cumprod', 'cumproduct', 'cumsum', 'datetime64', 'datetime_as_string', 'datetime_data', 'deg2rad', 'degrees', 'delete', 'deprecate', 'deprecate_with_doc', 'diag', 'diag_indices', 'diag_indices_from', 'diagflat', 'diagonal', 'diff', 'digitize', 'disp', 'divide', 'divmod', 'dot', 'double', 'dsplit', 'dstack', 'dtype', 'e', 'ediff1d', 'einsum', 'einsum_path', 'emath', 'empty', 'empty_like', 'equal', 'errstate', 'euler_gamma', 'exp', 'exp2', 'expand_dims', 'expm1', 'extract', 'eye', 'fabs', 'fastCopyAndTranspose', 'fft', 'fill_diagonal', 'find_common_type', 'finfo', 'fix', 'flatiter', 'flatnonzero', 'flexible', 'flip', 'fliplr', 'flipud', 'float16', 'float32', 'float64', 'float_', 'float_power', 'floating', 'floor', 'floor_divide', 'fmax', 'fmin', 'fmod', 'format_float_positional', 'format_float_scientific', 'format_parser', 'frexp', 'frombuffer', 'fromfile', 'fromfunction', 'fromiter', 'frompyfunc', 'fromregex', 'fromstring', 'full', 'full_like', 'gcd', 'generic', 'genfromtxt', 'geomspace', 'get_array_wrap', 'get_include', 'get_printoptions', 'getbufsize', 'geterr', 'geterrcall', 'geterrobj', 'gradient', 'greater', 'greater_equal', 'half', 'hamming', 'hanning', 'heaviside', 'histogram', 'histogram2d', 'histogram_bin_edges', 'histogramdd', 'hsplit', 'hstack', 'hypot', 'i0', 'identity', 'iinfo', 'imag', 'in1d', 'index_exp', 'indices', 'inexact', 'inf', 'info', 'infty', 'inner', 'insert', 'int0', 'int16', 'int32', 'int64', 'int8', 'int_', 'intc', 'integer', 'interp', 'intersect1d', 'intp', 'invert', 'is_busday', 'isclose', 'iscomplex', 'iscomplexobj', 'isfinite', 'isfortran', 'isin', 'isinf', 'isnan', 'isnat', 'isneginf', 'isposinf', 'isreal', 'isrealobj', 'isscalar', 'issctype', 'issubclass_', 'issubdtype', 'issubsctype', 'iterable', 'ix_', 'kaiser', 'kron', 'lcm', 'ldexp', 'left_shift', 'less', 'less_equal', 'lexsort', 'lib', 'linalg', 'linspace', 'little_endian', 'load', 'loads', 'loadtxt', 'log', 'log10', 'log1p', 'log2', 'logaddexp', 'logaddexp2', 'logical_and', 'logical_not', 'logical_or', 'logical_xor', 'logspace', 'longcomplex', 'longdouble', 'longfloat', 'longlong', 'lookfor', 'ma', 'mafromtxt', 'mask_indices', 'mat', 'math', 'matmul', 'matrix', 'matrixlib', 'max', 'maximum', 'maximum_sctype', 'may_share_memory', 'mean', 'median', 'memmap', 'meshgrid', 'mgrid', 'min', 'min_scalar_type', 'minimum', 'mintypecode', 'mod', 'modf', 'moveaxis', 'msort', 'multiply', 'nan', 'nan_to_num', 'nanargmax', 'nanargmin', 'nancumprod', 'nancumsum', 'nanmax', 'nanmean', 'nanmedian', 'nanmin', 'nanpercentile', 'nanprod', 'nanquantile', 'nanstd', 'nansum', 'nanvar', 'nbytes', 'ndarray', 'ndenumerate', 'ndfromtxt', 'ndim', 'ndindex', 'nditer', 'negative', 'nested_iters', 'newaxis', 'nextafter', 'nonzero', 'not_equal', 'numarray', 'number', 'obj2sctype', 'object0', 'object_', 'ogrid', 'oldnumeric', 'ones', 'ones_like', 'os', 'outer', 'packbits', 'pad', 'partition', 'percentile', 'pi', 'piecewise', 'place', 'poly', 'poly1d', 'polyadd', 'polyder', 'polydiv', 'polyfit', 'polyint', 'polymul', 'polynomial', 'polysub', 'polyval', 'positive', 'power', 'printoptions', 'prod', 'product', 'promote_types', 'ptp', 'put', 'put_along_axis', 'putmask', 'quantile', 'r_', 'rad2deg', 'radians', 'random', 'ravel', 'ravel_multi_index', 'real', 'real_if_close', 'rec', 'recarray', 'recfromcsv', 'recfromtxt', 'reciprocal', 'record', 'remainder', 'repeat', 'require', 'reshape', 'resize', 'result_type', 'right_shift', 'rint', 'roll', 'rollaxis', 'roots', 'rot90', 'round', 'round_', 'row_stack', 's_', 'safe_eval', 'save', 'savetxt', 'savez', 'savez_compressed', 'sctype2char', 'sctypeDict', 'sctypes', 'searchsorted', 'select', 'set_numeric_ops', 'set_printoptions', 'set_string_function', 'setbufsize', 'setdiff1d', 'seterr', 'seterrcall', 'seterrobj', 'setxor1d', 'shape', 'shares_memory', 'short', 'show_config', 'sign', 'signbit', 'signedinteger', 'sin', 'sinc', 'single', 'singlecomplex', 'sinh', 'size', 'sometrue', 'sort', 'sort_complex', 'source', 'spacing', 'split', 'sqrt', 'square', 'squeeze', 'stack', 'std', 'str0', 'str_', 'string_', 'subtract', 'sum', 'swapaxes', 'sys', 'take', 'take_along_axis', 'tan', 'tanh', 'tensordot', 'test', 'testing', 'tile', 'timedelta64', 'trace', 'tracemalloc_domain', 'transpose', 'trapz', 'tri', 'tril', 'tril_indices', 'tril_indices_from', 'trim_zeros', 'triu', 'triu_indices', 'triu_indices_from', 'true_divide', 'trunc', 'typeDict', 'typecodes', 'typename', 'ubyte', 'ufunc', 'uint', 'uint0', 'uint16', 'uint32', 'uint64', 'uint8', 'uintc', 'uintp', 'ulonglong', 'unicode_', 'union1d', 'unique', 'unpackbits', 'unravel_index', 'unsignedinteger', 'unwrap', 'use_hugepage', 'ushort', 'vander', 'var', 'vdot', 'vectorize', 'version', 'void', 'void0', 'vsplit', 'vstack', 'warnings', 'where', 'who', 'zeros', 'zeros_like']\n"
     ]
    }
   ],
   "source": [
    "print([d for d in dir(np) if not d.startswith(\"_\")])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 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9980, 9981, 9982, 9983, 9984, 9985, 9986, 9987, 9988, 9989, 9990, 9991, 9992, 9993, 9994, 9995, 9996, 9997, 9998]\n",
      "Wall time: 4 ms\n"
     ]
    }
   ],
   "source": [
    "%time print([i for i in range(9999)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([     0,      1,      2, ..., 999996, 999997, 999998])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time np.arange(999999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 0, 1, 2, 3, 4]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[i for i in range(5)] * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 4, 5, 6])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(5) + 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 2, 4, 6, 8]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_sample = [i for i in range(5)]\n",
    "for i in range(len(list_sample)):\n",
    "    list_sample[i] *= 2\n",
    "print(list_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 5)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "arr = np.arange(5)\n",
    "arr2 = np.array([arr,arr,arr,arr])\n",
    "arr2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr.size?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 5)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2 = np.array([np.arange(5),np.arange(1,6),np.arange(2,7),np.arange(3,8)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2, 3]), array([1, 3]))"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3 = arr2.reshape(2,5,2)\n",
    "arr3[0][0:2][1],arr3[0,0:2,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(754, 1194, 3)\n",
      "[[[171 167 166]\n",
      "  [170 166 165]\n",
      "  [170 166 165]\n",
      "  ...\n",
      "  [127 121 121]\n",
      "  [126 120 120]\n",
      "  [126 120 120]]\n",
      "\n",
      " [[171 167 166]\n",
      "  [170 166 165]\n",
      "  [170 166 165]\n",
      "  ...\n",
      "  [127 121 121]\n",
      "  [126 120 120]\n",
      "  [126 120 120]]\n",
      "\n",
      " [[171 167 166]\n",
      "  [171 167 166]\n",
      "  [170 166 165]\n",
      "  ...\n",
      "  [127 121 121]\n",
      "  [127 121 121]\n",
      "  [127 121 121]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[180 183 188]\n",
      "  [180 183 188]\n",
      "  [180 183 188]\n",
      "  ...\n",
      "  [ 50  48  51]\n",
      "  [ 50  48  51]\n",
      "  [ 50  48  51]]\n",
      "\n",
      " [[176 179 184]\n",
      "  [177 180 185]\n",
      "  [177 180 185]\n",
      "  ...\n",
      "  [ 50  48  51]\n",
      "  [ 51  49  52]\n",
      "  [ 51  49  52]]\n",
      "\n",
      " [[176 179 184]\n",
      "  [177 180 185]\n",
      "  [177 180 185]\n",
      "  ...\n",
      "  [ 50  48  51]\n",
      "  [ 51  49  52]\n",
      "  [ 51  49  52]]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "cat_img = plt.imread(\"cat.jpg\")\n",
    "plt.imshow(cat_img)\n",
    "print(cat_img.shape)\n",
    "print(cat_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 3, 7],\n",
       "       [1, 1, 5]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "arr = np.array([[[0, 1],[2, 3],[4, 1],[2, 3],[4, 5]],[[2, 3],[4, 5],[6, 3],[4, 5],[6, 7]]])\n",
    "arr2 = arr[::-1,::2,1]\n",
    "arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('S17019973', '孫峰', 'M036FD04110A0E', None, 0, 'Feng_Sun@intra.pegatroncorp.com', None, None, None, None, None, None, None, None, None, None, datetime.datetime(2017, 5, 9, 10, 54, 6), 0, 'Y', datetime.datetime(2017, 5, 8, 11, 54, 12))\n"
     ]
    }
   ],
   "source": [
    "import cx_Oracle as cx\n",
    "\n",
    "conn = cx.connect(\"S17019973/6358971@SZINMB01:1521/INTEL\")\n",
    "cursor = conn.cursor()\n",
    "cursor.execute(\"SELECT * FROM OP WHERE OP='S17019973'\")\n",
    "op = cursor.fetchone()\n",
    "print(op)\n",
    "cursor.close()\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3., 3., 7.],\n",
       "       [1., 1., 5.]])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2 = np.array([[3, 3, 7.0],[1, 1, 5]])\n",
    "arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr.resize?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  3,  5,  7,  9, 11])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3 = arr1 + arr2\n",
    "arr3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0 / 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  2,  4,  6,  8],\n",
       "       [ 2,  4,  6,  8, 10]])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1 = np.array([[0,1,2,3,4],[1,2,3,4,5]])\n",
    "arr2 = np.array([[0,1,2,3,4],[1,2,3,4,5]])\n",
    "arr3 = arr1 + arr2\n",
    "arr3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  4,  6,  8, 10])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1 = np.arange(0,5) #array([0, 1, 2, 3, 4])\n",
    "arr2 = np.arange(2,11,2) #array([ 2,  4,  6,  8, 10])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#include <stdio.h>\n",
    "#define HIGH 2\n",
    "#define SIZE 5\n",
    "\n",
    "int main()\n",
    "{\n",
    "    const int arr1[HIGH][SIZE] = {{0,1,2,3,4},\n",
    "\t\t\t\t\t\t\t{5,6,7,8,9}};\n",
    "\tconst int arr2[HIGH][SIZE] = {{0,1,2,3,4},\n",
    "\t\t\t\t\t\t\t{0,1,2,3,4}};\n",
    "\tint arr3[HIGH][SIZE];\n",
    "\tfor (int i=0;i<HIGH;i++){\n",
    "\t\tfor (int j=0;j<SIZE;j++){\n",
    "\t\t\tarr3[i][j] = arr1[i][j] + arr2[i][j];\n",
    "\t\t\tprintf(\"数组相加，第 %d 行第 %d 列的元素为 %d\\n\",i+1,j+1,arr3[i][j]);\n",
    "\t\t}\n",
    "\t}\n",
    "    return 0;\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#include <stdio.h>\n",
    "#define SIZE 5\n",
    "\n",
    "int main()\n",
    "{\n",
    "    const int arr[SIZE] = {0,1,2,3,4};   //这是初始化一个一维数组\n",
    "    for (int i=0;i<SIZE;i++){\n",
    "    //C 里面的数组下标自然是从0开始\n",
    "    printf(\"这是数组里面第%d个元素：%d\\n\",i+1,arr[i]);\n",
    "  }    \n",
    "    return 0;\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#include <stdio.h>\n",
    "#define HIGH 2\n",
    "#define SIZE 5\n",
    "\n",
    "int main()\n",
    "{\n",
    "    const int arr[HIGH][SIZE] = {{0,1,2,3,4},\n",
    "                                {5,6,7,8,9}};\n",
    "                                //这是初始化一个二维数组\n",
    "                                //array[0][0]表示第一个元素\n",
    "    for (int i=0;i<HIGH;i++){\n",
    "    for (int j=0;j<SIZE;j++){\n",
    "    /*c中二维数组获取具体元素的方式为array[i][j]，第i行第j个元素，\n",
    "    这里的i,j是从0开始算的\n",
    "    */\n",
    "    printf(\"这是数组第%d行里面第%d个元素：%d\\n\",i+1,j+1,arr[i][j]);\n",
    "  }\n",
    "  }\n",
    "    return 0;\n",
    "}"
   ]
  }
 ],
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